On the Relative Succinctness of Sentential Decision Diagrams
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Abstract (arXiv)
Sentential decision diagrams (SDDs) introduced by Darwiche in 2011 are a promising representation type used in knowledge compilation. The relative succinctness of representation types is an important subject in this area. The aim of the paper is to identify which kind of Boolean functions can be represented by SDDs of small size with respect to the number of variables the functions are defined on. For this reason the sets of Boolean functions representable by different representation types in polynomial size are investigated and SDDs are compared with representation types from the classical knowledge compilation map of Darwiche and Marquis. Ordered binary decision diagrams (OBDDs) which are a popular data structure for Boolean functions are one of these representation types. SDDs are more general than OBDDs by definition but only recently, a Boolean function was presented with polynomial SDD size but exponential OBDD size. This result is strengthened in several ways. The main result is a quasipolynomial simulation of SDDs by equivalent unambiguous nondeterministic OBDDs, a nondeterministic variant where there exists exactly one accepting computation for each satisfying input. As a side effect an open problem about the relative succinctness between SDDs and free binary decision diagrams (FBDDs) which are more general than OBDDs is answered.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 3.
6 papers.
- 2022On the Computation of Necessary and Sufficient Explanationscited by 23
- 2023Logic for Explainable AIcited by 23
- 2021On Quantifying Literals in Boolean Logic and Its Applications to Explainable AIcited by 16
- 2023R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intentscited by 10
- 2022Tractable Boolean and Arithmetic Circuitscited by 9
- 2023Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilationcited by 3
- 2023Logic for Explainable AI9 May 2023
- 2023Efficient Computation of Shap Explanation Scores for Neural Network Classifiers via Knowledge Compilation11 Mar 2023
- 2023R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents1 Mar 2023
- 2022On the Computation of Necessary and Sufficient Explanations20 Mar 2022
- 2022Tractable Boolean and Arithmetic Circuits7 Feb 2022
- 2021On Quantifying Literals in Boolean Logic and Its Applications to Explainable AI23 Aug 2021